Speaker: Dr. Weiwei He, NYU Abu Dhabi, UAE

Abstract: RNA is dynamic biopolymer whose structures, motions, and interactions underlie molecular recognition, gene regulation, and catalysis. Connecting these dynamics to function in native, ion-rich environments remains difficult. Solution-based measurements are often low-resolution and ensemble-averaged, so atomistic molecular dynamics (MD) simulations represent a helpful technique complementary to experiments. However, simulations can lack direct experimental grounding across time and length scales and still suffer from limitations of force fields. In this talk, I will present a coupled framework that integrates all-atom MD with solution small- and wide-angle X-ray scattering (SAXS/WAXS), augmented by machine learning techniques to reconstruct physically grounded structural ensembles of RNA systems and link them to biological outcomes, including: (i) sequence-dependent order within disordered single-stranded RNA ensembles; and (ii) the structural plasticity of double-stranded DNA and RNA in solution. Together, these studies provide an integrated computational route that couples measurement and modeling, yields interpretable structure-function relationships for rational drug design, and enables predictive design of nucleic acid therapeutics.

Biography: Weiwei He is currently a Postdoctoral Associate in Computational Biophysics and AI for Bioscience at NYU Abu Dhabi. He received his Ph.D. in Computational Biophysics from NYU in the Serdal Kirmizialtin Group, and earned his B.Sc. degree from Southern University of Science and Technology (SUSTech) with Professor Xin-Yuan Liu. He was named to MIT Technology Review’s Innovators Under 35 MENA (TR35) in 2025. His research integrates modern computer simulations, machine learning (ML) techniques, and experimental data to understand, predict and manipulate the dynamics and behavior of macromolecules and biomolecular machines, especially nucleic acid (DNA/RNA) systems. He focuses on data-driven simulation methods for RNA/DNA structure determination and refinement, RNA force field development and re-parameterization, RNA function control via ML-designed artificial modifications, and multi-scale modeling of nucleic acid-vector assemblies for gene therapy. His research advances strategic directions in nucleic acid biology, therapeutic design and delivery, and integrative computational approaches that connect physics-based modeling with biomedical applications.